added more unittests to convnet implementation

beaubol · Oct 23, 2013 · 05d68a2 · 05d68a2
1 parent f29b2d0
commit 05d68a2
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Showing 7 changed files with 694 additions and 68 deletions.
diff --git a/bin/cifar-10.c b/bin/cifar-10.c
@@ -15,8 +15,9 @@ int main(int argc, char** argv)
 	ccv_convnet_param_t params[] = {
 		{
 			.type = CCV_CONVNET_CONVOLUTIONAL,
-			.bias = 1,
+			.bias = 0,
 			.sigma = 0.0001,
+			.dropout_rate = 0,
 			.input = {
 				.matrix = {
 					.rows = 31,
@@ -53,8 +54,9 @@ int main(int argc, char** argv)
 		},
 		{
 			.type = CCV_CONVNET_CONVOLUTIONAL,
-			.bias = 1,
+			.bias = 0,
 			.sigma = 0.01,
+			.dropout_rate = 0,
 			.input = {
 				.matrix = {
 					.rows = 15,
@@ -91,8 +93,9 @@ int main(int argc, char** argv)
 		},
 		{
 			.type = CCV_CONVNET_CONVOLUTIONAL,
-			.bias = 1,
+			.bias = 0,
 			.sigma = 0.01,
+			.dropout_rate = 0,
 			.input = {
 				.matrix = {
 					.rows = 7,
@@ -129,14 +132,18 @@ int main(int argc, char** argv)
 		},
 		{
 			.type = CCV_CONVNET_FULL_CONNECT,
-			.bias = 1,
+			.bias = 0,
 			.sigma = 0.01,
+			.dropout_rate = 0,
 			.input = {
 				.matrix = {
 					.rows = 3,
 					.cols = 3,
 					.channels = 64,
 				},
+				.node = {
+					.count = 3 * 3 * 64,
+				},
 			},
 			.output = {
 				.full_connect = {
@@ -146,6 +153,70 @@ int main(int argc, char** argv)
 		},
 	};
 	ccv_convnet_t* convnet = ccv_convnet_new(params, 7);
+	assert(argc == 5);
+	int num1 = atoi(argv[2]);
+	int num2 = atoi(argv[4]);
+	FILE* r1 = fopen(argv[1], "rb");
+	FILE* r2 = fopen(argv[3], "rb");
+	if (r1 && r2)
+	{
+		int i, j, k;
+		unsigned char bytes[32 * 32 + 1];
+		ccv_array_t* categorizeds = ccv_array_new(sizeof(ccv_categorized_t), num1, 0);
+		for (k = 0; k < num1; k++)
+		{
+			fread(bytes, 32 * 32 + 1, 1, r1);
+			ccv_categorized_t categorized;
+			categorized.c = bytes[0]; // the class
+			ccv_dense_matrix_t* a = ccv_dense_matrix_new(31, 31, CCV_32F | CCV_C3, 0, 0);
+			for (i = 0; i < 31; i++)
+				for (j = 0; j < 31; j++)
+					a->data.f32[(j + i * 31) * 3] = bytes[j + i * 32 + 1] / 255.0;
+			fread(bytes, 32 * 32, 1, r1);
+			for (i = 0; i < 31; i++)
+				for (j = 0; j < 31; j++)
+					a->data.f32[(j + i * 31) * 3 + 1] = bytes[j + i * 32] / 255.0;
+			fread(bytes, 32 * 32, 1, r1);
+			for (i = 0; i < 31; i++)
+				for (j = 0; j < 31; j++)
+					a->data.f32[(j + i * 31) * 3 + 2] = bytes[j + i * 32] / 255.0;
+			categorized.matrix = a;
+			ccv_array_push(categorizeds, &categorized);
+		}
+		ccv_array_t* tests = ccv_array_new(sizeof(ccv_categorized_t), num2, 0);
+		for (k = 0; k < num2; k++)
+		{
+			fread(bytes, 32 * 32 + 1, 1, r2);
+			ccv_categorized_t categorized;
+			categorized.c = bytes[0]; // the class
+			ccv_dense_matrix_t* a = ccv_dense_matrix_new(31, 31, CCV_32F | CCV_C3, 0, 0);
+			for (i = 0; i < 31; i++)
+				for (j = 0; j < 31; j++)
+					a->data.f32[(j + i * 31) * 3] = bytes[j + i * 32 + 1] / 255.0;
+			fread(bytes, 32 * 32, 1, r2);
+			for (i = 0; i < 31; i++)
+				for (j = 0; j < 31; j++)
+					a->data.f32[(j + i * 31) * 3 + 1] = bytes[j + i * 32] / 255.0;
+			fread(bytes, 32 * 32, 1, r2);
+			for (i = 0; i < 31; i++)
+				for (j = 0; j < 31; j++)
+					a->data.f32[(j + i * 31) * 3 + 2] = bytes[j + i * 32] / 255.0;
+			categorized.matrix = a;
+			ccv_array_push(tests, &categorized);
+		}
+		ccv_convnet_train_param_t params = {
+			.max_epoch = 100,
+			.mini_batch = 1,
+			.decay = 0.005,
+			.learn_rate = 0.001,
+			.momentum = 0.9,
+		};
+		ccv_convnet_supervised_train(convnet, categorizeds, tests, params);
+	}
+	if (r1)
+		fclose(r1);
+	if (r2)
+		fclose(r2);
 	ccv_convnet_free(convnet);
 	ccv_disable_cache();
 	return 0;

diff --git a/lib/ccv.h b/lib/ccv.h
@@ -1143,6 +1143,9 @@ typedef struct {
 typedef struct {
 	int max_epoch;
 	int mini_batch;
+	double decay;
+	double learn_rate;
+	double momentum;
 } ccv_convnet_train_param_t;
 
 typedef struct {
@@ -1154,7 +1157,7 @@ typedef struct {
 } ccv_categorized_t;
 
 ccv_convnet_t* __attribute__((warn_unused_result)) ccv_convnet_new(ccv_convnet_param_t params[], int count);
-void ccv_convnet_supervised_train(ccv_convnet_t* convnet, ccv_array_t* categorizeds, ccv_convnet_train_param_t params);
+void ccv_convnet_supervised_train(ccv_convnet_t* convnet, ccv_array_t* categorizeds, ccv_array_t* tests, ccv_convnet_train_param_t params);
 void ccv_convnet_encode(ccv_convnet_t* convnet, ccv_dense_matrix_t* a, ccv_dense_matrix_t** b, int type);
 int ccv_convnet_classify(ccv_convnet_t* convnet, ccv_dense_matrix_t* a);
 void ccv_convnet_free(ccv_convnet_t* convnet);

diff --git a/lib/ccv_algebra.c b/lib/ccv_algebra.c
@@ -293,7 +293,7 @@ void ccv_gemm(ccv_matrix_t* a, ccv_matrix_t* b, double alpha, ccv_matrix_t* c, d
 	switch (CCV_GET_DATA_TYPE(dd->type))
 	{
 		case CCV_32F:
-			cblas_sgemm(CblasRowMajor, (transpose & CCV_A_TRANSPOSE) ? CblasTrans : CblasNoTrans, (transpose & CCV_B_TRANSPOSE) ? CblasTrans : CblasNoTrans, dd->rows, dd->cols, da->cols, alpha, da->data.f32, da->cols, db->data.f32, db->cols, beta, dd->data.f32, dd->cols);
+			cblas_sgemm(CblasRowMajor, (transpose & CCV_A_TRANSPOSE) ? CblasTrans : CblasNoTrans, (transpose & CCV_B_TRANSPOSE) ? CblasTrans : CblasNoTrans, dd->rows, dd->cols, (transpose & CCV_A_TRANSPOSE) ? da->rows : da->cols, alpha, da->data.f32, da->cols, db->data.f32, db->cols, beta, dd->data.f32, dd->cols);
 			break;
 		case CCV_64F:
 			cblas_dgemm(CblasRowMajor, (transpose & CCV_A_TRANSPOSE) ? CblasTrans : CblasNoTrans, (transpose & CCV_B_TRANSPOSE) ? CblasTrans : CblasNoTrans, dd->rows, dd->cols, (transpose & CCV_A_TRANSPOSE) ? da->rows : da->cols, alpha, da->data.f64, da->cols, db->data.f64, db->cols, beta, dd->data.f64, dd->cols);